Elastic Volume-Weighted Student-T TensionOverview
The Elastic Volume-Weighted Student-T Tension Bands indicator dynamically adapts to market conditions using an advanced statistical model based on the Student-T distribution. Unlike traditional Bollinger Bands or Keltner Channels, this indicator leverages elastic volume-weighted averaging to compute real-time dispersion and location parameters, making it highly responsive to volatility changes while maintaining robustness against price fluctuations.
This methodology is inspired by incremental calculation techniques for weighted mean and variance, as outlined in the paper by Tony Finch:
📄 "Incremental Calculation of Weighted Mean and Variance" .
Key Features
✅ Adaptive Volatility Estimation – Uses an exponentially weighted Student-T model to dynamically adjust band width.
✅ Volume-Weighted Mean & Dispersion – Incorporates real-time volume weighting, ensuring a more accurate representation of market sentiment.
✅ High-Timeframe Volume Normalization – Provides an option to smooth volume impact by referencing a higher timeframe’s cumulative volume, reducing noise from high-variability bars.
✅ Customizable Tension Parameters – Configurable standard deviation multipliers (σ) allow for fine-tuned volatility sensitivity.
✅ %B-Like Oscillator for Relative Price Positioning – The main indicator is in form of a dedicated oscillator pane that normalizes price position within the sigma ranges, helping identify overbought/oversold conditions and potential momentum shifts.
✅ Robust Statistical Foundation – Utilizes kurtosis-based degree-of-freedom estimation, enhancing responsiveness across different market conditions.
How It Works
Volume-Weighted Elastic Mean (eμ) – Computes a dynamic mean price using an elastic weighted moving average approach, influenced by trade volume, if not volume detected in series, study takes true range as replacement.
Dispersion (eσ) via Student-T Distribution – Instead of assuming a fixed normal distribution, the bands adapt to heavy-tailed distributions using kurtosis-driven degrees of freedom.
Incremental Calculation of Variance – The indicator applies Tony Finch’s incremental method for computing weighted variance instead of arithmetic sum's of fixed bar window or arrays, improving efficiency and numerical stability.
Tension Calculation – There are 2 dispersion custom "zones" that are computed based on the weighted mean and dynamically adjusted standard student-t deviation.
%B-Like Oscillator Calculation – The oscillator normalizes the price within the band structure, with values between 0 and 1:
* 0.00 → Price is at the lower band (-2σ).
* 0.50 → Price is at the volume-weighted mean (eμ).
* 1.00 → Price is at the upper band (+2σ).
* Readings above 1.00 or below 0.00 suggest extreme movements or possible breakouts.
Recommended Usage
For scalping in lower timeframes, it is recommended to use the fixed α Decay Factor, it is in raw format for better control, but you can easily make a like of transformation to N-bar size window like in EMA-1 bar dividing 2 / decayFactor or like an RMA dividing 1 / decayFactor.
The HTF selector catch quite well Higher Time Frame analysis, for example using a Daily chart and using as HTF the 200-day timeframe, weekly or monthly.
Suitable for trend confirmation, breakout detection, and mean reversion plays.
The %B-like oscillator helps gauge momentum strength and detect divergences in price action if user prefer a clean chart without bands, this thanks to pineScript v6 force overlay feature.
Ideal for markets with volume-driven momentum shifts (e.g., futures, forex, crypto).
Customization Parameters
Fixed α Decay Factor – Controls the rate of volume weighting influence for an approximation EWMA approach instead of using sum of series or arrays, making the code lightweight & computing fast O(1).
HTF Volume Smoothing – Instead of a fixed denominator for computing α , a volume sum of the last 2 higher timeframe closed candles are used as denominator for our α weight factor. This is useful to review mayor trends like in daily, weekly, monthly.
Tension Multipliers (±σ) – Adjusts sensitivity to dispersion sigma parameter (volatility).
Oscillator Zone Fills – Visual cues for price positioning within the cloud range.
Posible Interpretations
As market within indicators relay on each individual edge, this are just some key ideas to glimpse how the indicator could be interpreted by the user:
📌 Price inside bands – Market is considered somehow "stable"; price is like resting from tension or "charging batteries" for volume spike moves.
📌 Price breaking outer bands – Potential breakout or extreme movement; watch for reversals or continuation from strong moves. Market is already in tension or generating it.
📌 Narrowing Bands – Decreasing volatility; expect contraction before expansion.
📌 Widening Bands – Increased volatility; prepare for high probability pull-back moves, specially to the center location of the bands (the mean) or the other side of them.
📌 Oscillator is just the interpretation of the price normalized across the Student-T distribution fitting "curve" using the location parameter, our Elastic Volume weighted mean (eμ) fixed at 0.5 value.
Final Thoughts
The Elastic Volume-Weighted Student-T Tension indicator provides a powerful, volume-sensitive alternative to traditional volatility bands. By integrating real-time volume analysis with an adaptive statistical model, incremental variance computation, in a relative price oscillator that can be overlayed in the chart as bands, it offers traders an edge in identifying momentum shifts, trend strength, and breakout potential. Think of the distribution as a relative "tension" rubber band in which price never leave so far alone.
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The following indicator was made for NON LUCRATIVE ACTIVITIES and must remain as is, following TradingView's regulations. Use of indicator and their code are published for work and knowledge sharing. All access granted over it, their use, copy or re-use should mention authorship(s) and origin(s).
WARNING NOTICE!
THE INCLUDED FUNCTION MUST BE CONSIDERED FOR TESTING. The models included in the indicator have been taken from open sources on the web and some of them has been modified by the author, problems could occur at diverse data sceneries, compiler version, or any other externality.
Volatilità
WAVE시그널(추세)WAVE 시그널(추세) - SuperTrend 기반 트렌드 지표
🔹 개요
WAVE 시그널(추세)은 ATR(평균 진폭)과 SuperTrend 알고리즘을 기반으로 시장의 상승 및 하락 추세를 분석하는 지표입니다.
ATR을 활용한 변동성 조절 기능과 매수/매도 신호를 제공하여 트레이더들이 보다 정확한 진입 및 청산을 결정할 수 있도록 돕습니다.
🔹 주요 기능
✅ SuperTrend 기반 추세 감지: 상승/하락 추세를 ATR을 이용하여 효과적으로 탐지
✅ 매수/매도 신호 표시: 추세 전환 시 매수(초록) 및 매도(빨강) 라벨을 표시하여 진입 타이밍 제공
✅ 변동성 반영 가능: ATR 계산 방식을 선택하여 사용자 맞춤형 조정 가능
✅ 시각적 강조 효과: 상승/하락 추세에 따른 차트 하이라이팅 기능
✅ 알림(Alert) 기능: 추세 전환 및 매수/매도 신호 발생 시 알람 전송
🔹 활용 방법
📌 추세 매매: SuperTrend 선이 상승할 때 매수, 하락할 때 매도 신호로 활용
📌 변동성 매매: ATR 값을 조절하여 변동성이 높은 구간에서도 대응 가능
📌 알림 설정: 매매 신호 또는 추세 변화를 감지하는 자동화된 트레이딩 전략에 적용
💡 본 스크립트는 단독으로 사용하기보다 다른 보조 지표와 함께 활용하면 더욱 효과적입니다.
Vinayz Options StratergyBack Tested Results with Banknifty from Year 2001 till March 21 2025
Metric Result (5-min) Result (15-min)
📈 Win Rate 77.6% 75.9%
💰 Profit Factor 3.52 3.09
📉 Max Drawdown -8.1% -9.4%
🕰️ Avg Trade Duration 2.7 hours 3.1 hours
🔥 Risk-Reward Ratio 1:2.25 1:2.15
💸 Total Trades 21,420 18,375
🚨 Max Consecutive Loss 4 trades 5 trades
This strategy doesn't guarantee profits nor this is a recommendation to use this for trading.
Use this at your own risk
Short-Term Trend Predictor: Call/Put (5min to 4hr)Strategy Description
This trading strategy is designed to identify short-term trends on charts ranging from 5 minutes to 4 hours, and to provide entry signals for call or put options. It uses a combination of key technical indicators to increase the probability of successful trades.
Indicators Used:
Simple Moving Averages (SMA): Two SMAs (one short and one long) are used to identify the direction of the short-term trend. The crossover of these moving averages can signal a change in trend.
Relative Strength Index (RSI): The RSI measures the speed and magnitude of recent price movements to evaluate overbought or oversold conditions.
SMA 200: Used to determine the overall market trend (bullish or bearish) over a longer period.
High and Low Resistance: Resistance and support levels are calculated to identify potential areas where the price may encounter difficulty in rising or falling.
MACD: The MACD indicator is used to identify changes in the strength, direction, momentum, and duration of a price trend in an asset.
Stochastic: The stochastic oscillator is used to compare a security's closing price to its range of prices over a certain period of time.
ADX: The Average Directional Index is used to measure the strength of a trend.
ATR: The Average True Range is used to measure market volatility.
Trading Signals:
The strategy generates trading signals based on moving average crossovers, overbought/oversold conditions of the RSI and Stochastic, the direction of the trend relative to the SMA 200, and the strength of the trend measured by the ADX.
Buy Signal (Call): A buy signal is generated when the short SMA crosses above the long SMA, the RSI is not overbought, the Stochastic is not overbought, the price is above the SMA 200, and the ADX indicates a strong trend.
Sell Signal (Put): A sell signal is generated when the short SMA crosses below the long SMA, the RSI is not oversold, the Stochastic is not oversold, the price is below the SMA 200, and the ADX indicates a strong trend.
Risk Management:
The strategy includes risk management mechanisms to protect investor capital:
Stop Loss: A stop loss is placed at a level based on the ATR and a multiplier, in order to limit potential losses.
Take Profit: A take profit is placed at a level based on the ATR, a multiplier and the Take Profit/Risk ratio, in order to secure potential gains.
Position Size: The position size is calculated based on the available capital and the percentage of risk per trade, in order to control risk exposure.
Backtesting:
The strategy has been backtested on historical data to evaluate its performance. The backtest results include metrics such as the number of trades, win rate, total profit, maximum drawdown, average profit per trade, average loss per trade, and profit factor.
Disclaimer:
Trading in financial markets involves risks. Past performance is not indicative of future results. This strategy is provided for informational and educational purposes only, and does not constitute financial advice. It is recommended to do your own research and consult with a qualified financial advisor before making any investment decisions.
Vix_Fix with Wick ConfirmationThe idea is to create a volatility top and bottom indicator on the chart. Since the Vix is very nervous and almost useless you can filter the wick confirmation of the market and trade a double bottom or Supply and Demand Zone. I advise to wait until the candle closes and buy the next candles. However, if the price dives below or above an expected resistance level, it could be bought right away, when 2 or 3 ticks Vola-Wick Movement is confirmed by the indicator. The indicator comes with an alert function. If you put 0.00 at Wick-Settings the indicator will show you all Vix-Volatility Signals.
WAVE(구름)시그널WAVE(구름) 시그널 - Volumatic VIDYA by BigBeluga
🔹 개요
WAVE(구름) 시그널은 변동성 기반의 VIDYA(Variable Index Dynamic Average) 지표를 활용하여 시장의 트렌드를 시각적으로 표현하는 맞춤형 인디케이터입니다. 변동성과 모멘텀을 고려한 스마트한 필터링을 통해 트렌드 반전, 지지/저항 레벨, 유동성 영역 등을 효과적으로 탐지할 수 있습니다.
🔹 기능 및 특징
✅ VIDYA 기반 트렌드 분석: 시장의 모멘텀을 반영하여 지속적인 상승/하락 트렌드를 감지
✅ 유동성 영역 시각화: 주요 지지/저항 구간을 표시하여 고액 거래 구역을 쉽게 식별 가능
✅ ATR(평균 진폭) 기반 밴드: 변동성을 반영한 상단/하단 밴드를 활용해 트렌드 강도 분석
✅ 트렌드 변화 감지: 상승/하락 전환 지점에 ▲▼ 마커를 배치하여 매매 타이밍 포착
✅ 볼륨 기반 필터링: 거래량 변화를 감지하여 매매 신호의 신뢰도를 보완
🔹 활용 방법
📌 트렌드 매매: VIDYA 라인이 상승 전환 시 매수, 하락 전환 시 매도 시그널로 활용
📌 유동성 분석: 주요 저항/지지선에서 거래량 분포를 확인하여 진입 및 청산 전략 수립
📌 과매수/과매도 감지: ATR 기반 상/하단 밴드를 돌파하는 움직임을 통해 변동성 분석
Adaptive Bollinger BandsAdaptive Bollinger Bands
This indicator displays Bollinger Bands with parameters that dynamically adjust based on market volatility. Unlike standard Bollinger Bands with fixed parameters, this version adaptively modifies both the period and standard deviation multiplier in real-time based on measured market conditions.
Key Features
Dynamic adjustment of period and standard deviation based on normalized volatility
Color-coded visualization of current volatility regime (expanding, normal, contracting)
Integration with Keltner Channels for band refinement
Bandwidth analysis for volatility regime identification
Optional on-chart parameter labels showing current settings
Band cross alerts and visual markers
Volatility Visualization
The indicator uses color-coding to display different volatility regimes:
Red: Expanding volatility regime (higher measured volatility)
Blue: Normal volatility regime (average measurements)
Green: Contracting volatility regime (lower measured volatility)
Technical Information
The indicator calculates volatility by analyzing price returns over a configurable lookback period (default 50 bars). The standard deviation of returns is normalized against historical extremes to create an adaptive scaling factor.
Band adaptation occurs through two primary mechanisms:
1. Period adjustment: Higher volatility uses shorter periods (more responsive), while lower volatility uses longer periods (more stable)
2. Standard deviation multiplier adjustment: Higher volatility increases the multiplier (wider bands), while lower volatility decreases it (tighter bands)
The middle band uses a simple moving average with the adaptive period. Additional refinement occurs through Keltner Channel integration, which can tighten bands when contained within Keltner boundaries.
Volatility regimes are determined by analyzing Bollinger Bandwidth relative to its recent history, providing contextual information about the current market state.
Settings Customization
The indicator provides extensive customization options:
- Base parameters (period and standard deviation)
- Adaptive range limits (min/max period and standard deviation)
- Keltner Channel parameters for band refinement
- Bandwidth analysis settings
- Display options for visual elements
Limitations and Considerations
All technical indicators have inherent limitations and should not be used in isolation
Past performance does not guarantee future results
The indicator requires sufficient historical data for proper volatility normalization
Smaller timeframes may produce more noise in the adaptive calculations
Parameters may require adjustment for different markets and trading styles
Band crosses are not trading signals on their own and should be evaluated with other factors
This indicator is designed to provide objective information about market volatility conditions and potential support/resistance zones. Always combine with other analysis methods within a comprehensive trading approach.
FlexATRFlexATR: A Dynamic Multi-Timeframe Trading Strategy
Overview: FlexATR is a versatile trading strategy that dynamically adapts its key parameters based on the timeframe being used. It combines technical signals from exponential moving averages (EMAs) and the Relative Strength Index (RSI) with volatility-based risk management via the Average True Range (ATR). This approach helps filter out false signals while adjusting to varying market conditions — whether you’re trading on a daily chart, intraday charts (30m, 60m, or 5m), or even on higher timeframes like the 4-hour or weekly charts.
How It Works:
Multi-Timeframe Parameter Adaptation: FlexATR is designed to automatically adjust its indicator settings depending on the timeframe:
Daily and Weekly: On higher timeframes, the strategy uses longer periods for the fast and slow EMAs and standard periods for RSI and ATR to capture more meaningful trend confirmations while minimizing noise.
Intraday (e.g., 30m, 60m, 5m, 4h): The parameters are converted from “days” into the corresponding number of bars. For instance, on a 30-minute chart, a “day” might equal 48 bars. The preset values for a 30-minute chart have been slightly reduced (e.g., a fast EMA is set at 0.35 days instead of 0.4) to improve reactivity while maintaining robust filtering.
Signal Generation:
Entry Signals: The strategy enters long positions when the fast EMA crosses above the slow EMA and the RSI is above 50, and it enters short positions when the fast EMA crosses below the slow EMA with the RSI below 50. This dual confirmation helps ensure that signals are reliable.
Risk Management: The ATR is used to compute dynamic levels for stop loss and profit target:
Stop Loss: For a long position, the stop loss is placed at Price - (ATR × Stop Loss Multiplier). For a short position, it is at Price + (ATR × Stop Loss Multiplier).
Profit Target: The profit target is similarly set using the ATR multiplied by a designated profit multiplier.
Dynamic Trailing Stop: FlexATR further incorporates a dynamic trailing stop (if enabled) that adjusts according to the ATR. This trailing stop follows favorable price movements at a distance defined by a multiplier, locking in gains as the trend develops. The use of a trailing stop helps protect profits without requiring a fixed exit point.
Capital Allocation: Each trade is sized at 10% of the total equity. This percentage-based position sizing allows the strategy to scale with your account size. While the current setup assumes no leverage (a 1:1 exposure), the inherent design of the strategy means you can adjust the leverage externally if desired, with risk metrics scaling accordingly.
Visual Representation: For clarity and accessibility (especially for those with color vision deficiencies), FlexATR employs a color-blind friendly palette (the Okabe-Ito palette):
EMA Fast: Displayed in blue.
EMA Slow: Displayed in orange.
Stop Loss Levels: Rendered in vermilion.
Profit Target Levels: Shown in a distinct azzurro (light blue).
Benefits and Considerations:
Reliability: By requiring both EMA crossovers and an RSI confirmation, FlexATR filters out a significant amount of market noise, which reduces false signals at the expense of some delayed entries.
Adaptability: The automatic conversion of “day-based” parameters into bar counts for intraday charts means the strategy remains consistent across different timeframes.
Risk Management: Using the ATR for both fixed and trailing stops allows the strategy to adapt to changing market volatility, helping to protect your capital.
Flexibility: The strategy’s inputs are customizable via the input panel, allowing traders to fine-tune the parameters for different assets or market conditions.
Conclusion: FlexATR is designed as a balanced, adaptive strategy that emphasizes reliability and robust risk management across a variety of timeframes. While it may sometimes enter trades slightly later due to its filtering mechanism, its focus on confirming trends helps reduce the likelihood of false signals. This makes it particularly attractive for traders who prioritize a disciplined, multi-timeframe approach to capturing market trends.
Chandelier ExitRandSig Multi Indicator is a combination of indicator in one. It has parabolic SAR, Chandelier, Moving Averages cross as well as Bollinger Bands all in one indicator
Khaos Trading Botbot that reads strong trends and retracements using moving averages 20 50 in confluence with major support and resistance and a touch of fib to tie it all together i just use it as an indicator since we can only can 2 at a time free lol ,make sure you test profitability on specific time frames you want to trade to avoid trading in bad conditions
3 Level ZZ Semafor-V13 Level ZZ Semfor Version 1
Semafors:
Unlike what it appears like in the chart, Semafors are not points of reversals. They are simply high and low points.
Which is why, when a new high/low is created, a semafor will shift to the new bar. So one should be very careful trading semafors.
As it’s important to wait and see that the reversal is actually real.
You can use tools like:
Halftrend or Supertrend to see if trend indeed has reversed.
Pivot points or Fibo or SR levels to see strong Support/Resistance points. Where reversal is most likely
Strong volume bars which means that Buyers for a Bullish (Green) bar or Sellers for a bearish (Red) bar are increasing which makes reversal stronger
3LZZ uses 3 types of points.
Semafor 1: smaller high/lows
Semafor 2: medium
Semafor 3: Longer
Semafor 1: Depth: 5, Deviation: 1, Backstep: 3
Semafor 2: Depth: 13, Deviation: 8, Backstep: 5
Semafor 3: Depth: 34, Deviation: 13, Backstep: 8
You will notice that these are all numbers from the Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, and so on :)
Deviation (Percentage) — The minimal price change required for the indicator to form a high/low.
Percentage of change that represents when the trend changes.
For example, if the price drops 5% from the high, then the Zigzag indicator show a red line falling, showing that the trend has changed.
Depth (No. of Bars) — Minimum interval during which the indicator will draw a new high/low if the Deviation changes.
So within “depth” (period) number of bars, there cannot be another high/low.
Backstep (No. of Bars) — Number of bars (candlesticks) required to separate two local extremes. At this interval, new highs/lows will not be drawn if they differ in size from the prior ones. So backstep needs to be less than depth. Or else it can’t work.
Let’s take default values:
Depth: 12
Deviation: 5
Backstep: 3
So, it will look at past 12 (depth) bars and find highest high and lowest low levels. Distance between the two levels should be more than 3 (backstep) bars.
Now, let’s say that the latest extreme level was a low. A few bars back (more than backstep 3).
And now at current bar, price deviates more than 5% upwards. Then it will create a red zigzag line to the high of this newest bar.
If price in next bar still shifts upwards, then zigzag line will move to the new high.
Let’s say that more than 3 (backstep) bars have passed again. And price now goes 5% deviation lower. Then a new red zigzag line will be drawn to the low of this newest bar.
And so on…
Improved Gravity Center for BTCThis script is an advanced adaptation of the Belkhayat Gravity Center indicator tailored for BTC. It leverages ATR-based volatility to dynamically adjust support/resistance bands around a linear regression center, providing more reliable entries and exits. In addition, it incorporates multi-timeframe confirmation, 200 EMA trend filtering, and an RSI momentum check, resulting in a more robust and institutional‐grade approach to trading Bitcoin’s volatile price action.
Deviation ChannelsIndicator Name: Deviation Channels (Dev Chan)
Why Use This Indicator?
Visualize Volatility Ranges:
The indicator plots Keltner Channels at four levels above and below an average line, letting you easily see how far price has deviated from a typical range. Each “dev” line highlights potential support or resistance during pullbacks or surges.
Color-Coded Clarity:
Each band shifts color intensity depending on whether the current price is trading above or below it, letting you spot breakouts and rejections at a glance. Meanwhile, the Fast SMA (default 10) also changes color – green if price is above, red if below – adding a quick momentum read.
Adjustable Source & Length:
Choose your input source (open, close, ohlc4, or hlc3) and set your Keltner length to suit different asset classes or timeframes. Whether you want a tighter, more reactive channel or a smoother, longer-term reading, the script adapts with minimal effort.
A Simple Trading Approach
Identify Trend with Fast SMA:
If the Fast SMA (default length 10) is green (price above it), treat that as a bullish environment. If it’s red (price below), favor bearish or neutral stances.
Wait for Price to Reach Lower/Upper Deviations:
In a bullish setup (Fast SMA green), watch for price to dip into one of the lower channels (e.g., -1 Dev or -2 Dev). Such pullbacks can become potential “buy the dip” zones if price stabilizes and resumes upward momentum.
Conversely, if the Fast SMA is red, watch for price to test the upper channels (1 Dev or 2 Dev). That might be a short opportunity or a place to close out any remaining longs before a deeper correction.
Manage Risk with Channel Levels:
Place stop-losses just beyond the next “dev” band to protect against volatility. For example, if you enter on a bounce at -1 Dev, consider placing a stop near -2 Dev or -3 Dev, depending on your risk tolerance.
Take Profits Gradually:
In an uptrend, you might scale out of positions as price moves toward higher lines (e.g., 1 Dev or 2 Dev). Conversely, if price fails to hold above the Fast SMA or repeatedly closes below a key band, it might be time to exit.
Disclaimer: No single indicator is foolproof. Always combine with sound risk management, observe multiple timeframes, and consider fundamental factors before making trading decisions. Experiment with the Keltner length and Fast SMA fastLength to find the sweet spot for your market and time horizon.
Moving Average Square-Logarithmic Convergence DivergenceMore readable MACD version
Which uses logarithmic levels instead of MAs' subtract:
1 level = change `(( 5 ^ 0.5 + 1 ) / 2) ^ 0.001` times ( 1 / 1000th of greater golden ratio)
-1 level = change `(( 5 ^ 0.5 - 1 ) / 2) ^ 0.001` times ( 1 / 1000th of lesser golden ratio)
Use cases for:
- Small time frames for day-long positions (~3m candles) - direction only to mind risks.
- Small frame for in-month (~35-45m candles) - useless.
- Avg and large for month+ long positions (4h / 1d candles) - peaks divergence/convergence to spot trend exhaustion.
Grid Chop Index (GCI)// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// The Grid Chop Index (GCI) is a custom technical indicator designed for TradingView,
// specifically tailored for traders using grid bots in the cryptocurrency market.
// It measures the degree of price choppiness—defined as the total intraday price fluctuations around the previous close—over a specified lookback period.
// Unlike traditional volatility measures (e.g., Average True Range or standard deviation),
// GCI focuses on the cumulative range of price movements within each candle,
// making it particularly useful for identifying market conditions where grid bots can maximize profitability through frequent buy/sell cycles.
//@version=5
indicator("Grid Chop Index (GCI)", shorttitle="GCI", overlay=false)
// Inputs
lookback_bars = input.int(48, "Lookback Period (bars)", minval=1, tooltip="Number of bars to sum choppiness over (e.g., 48 for 1h = 2 days)")
smooth = input.int(3, "Smoothing Period", minval=1, maxval=10, tooltip="Smooths candle range data")
show_labels = input.bool(true, "Show Labels", tooltip="Show GCI value labels")
// Calculate bars per day based on timeframe.period
interval_str = timeframe.period
bars_per_day = interval_str == "1" ? 1440 : // 1-minute
interval_str == "5" ? 288 : // 5-minute
interval_str == "15" ? 96 : // 15-minute
interval_str == "30" ? 48 : // 30-minute
interval_str == "60" ? 24 : // 1-hour
interval_str == "240" ? 6 : // 4-hour
interval_str == "1D" ? 1 : 1 // Daily or other, fallback to 1
// Dynamic lookback adjustment based on timeframe
lookback = lookback_bars < bars_per_day ? lookback_bars : bars_per_day
// Calculate candle range as percentage of previous close
candleRangePercentRaw = (high - low) / close * 100
candleRangePercent = ta.sma(candleRangePercentRaw, smooth)
// Sum the percentage ranges and cap to avoid extreme values
totalChopPercentRaw = math.sum(candleRangePercent, lookback)
totalChopPercent = math.min(totalChopPercentRaw, 100) // Cap at 100% for readability
// Short-term GCI for recent choppiness
shortLookback = math.max(6, lookback / 4)
shortChopPercent = math.min(math.sum(candleRangePercent, shortLookback), 100)
// Plot GCI with dynamic coloring (rewritten with alternative color)
gciColor = totalChopPercent > ta.sma(totalChopPercent, lookback) ? color.orange : color.blue
plot(totalChopPercent, title="GCI", color=gciColor, linewidth=2, style=plot.style_line)
// Plot moving average of GCI (offset for visibility)
avgGCI = ta.sma(totalChopPercent, lookback)
plot(avgGCI + 1, title="Avg GCI", color=color.white, style=plot.style_circles)
// Plot short-term GCI (offset for visibility)
plot(shortChopPercent + 2, title="Short GCI", color=color.purple, style=plot.style_circles)
// Highlight high chop zones (based on average instead of threshold)
bgcolor(totalChopPercent > avgGCI * 1.5 ? color.new(color.green, 90) : na)
// Improved label for recent choppiness (conditional display)
if show_labels and barstate.islast
label.new(bar_index, totalChopPercent + 2, text=str.tostring(totalChopPercent, "#.##") + "%",
color=color.black, textcolor=color.white, style=label.style_label_down,
yloc=yloc.price, size=size.tiny, textalign=text.align_center)
// Add subtle grid for context
hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dotted)
hline(50, "Mid Line", color=color.gray, linestyle=hline.style_dotted)
// Alert condition (adjusted to use average)
alertcondition(totalChopPercent > avgGCI * 1.5, title="High Chop Alert", message="GCI exceeds 1.5x average at {{close}}%")
swing trade strategy that's profitableuse on the 4h ONLY
message me on twitter to request features, updates etc.
Spacetime IndicatorThe Spacetime Indicator is designed to identify significant market movements, trends, and potential reversals by integrating concepts from physics—specifically, ideas inspired by relativity and spacetime—into financial market analysis. The core idea is to treat price and volume dynamics as a system that can be analyzed through the lens of "mass," "energy," and "momentum," analogous to physical systems. Here’s a breakdown of the key ideas:
Price and Volume as a Spacetime System:
The indicator conceptualizes the market as a spacetime-like environment where price movements (akin to spatial displacement) and volume (akin to a force or intensity) interact over time.
By combining price changes, volume, and volatility, the indicator creates a "mass" metric that represents the market’s intensity or significance of movement. This mass is influenced by the speed of price changes (velocity), the magnitude of volume, and the market’s volatility, drawing a parallel to how mass in physics is affected by velocity in relativistic contexts.
Relativistic Effects in Market Dynamics:
Inspired by Einstein’s theory of relativity, the indicator incorporates a relativistic factor to amplify the mass during rapid price movements. Just as an object’s mass increases as it approaches the speed of light in physics, the indicator increases the "mass" of a price move when its velocity (relative to volatility) is high, emphasizing significant market shifts.
This relativistic approach helps highlight moves that are unusually fast or impactful, which are often associated with strong trends or potential reversals.
Mass Flow as a Measure of Trend Strength:
The indicator introduces the concept of "mass flow," which is derived from the mass metric and smoothed over different time periods (fast and slow). The difference between fast and slow mass flows creates a Mass Flow Oscillator (MFO), which measures the strength and direction of the trend.
A high MFO value (positive or negative) indicates a strong trend, while divergence between price and MFO can signal potential reversals, as the underlying "energy" of the move weakens.
Energy and Momentum for Signal Generation:
The indicator calculates an "energy" metric (the absolute value of the MFO) to quantify the strength of a move. High energy indicates a significant market movement, which is used to filter out weak signals and focus on impactful trades.
Momentum, calculated as the normalized price velocity multiplied by volume, further refines the signal generation by ensuring that moves are supported by both price action and volume.
Reversal Detection Through Divergence:
The indicator uses divergence between price and the MFO to identify potential reversals. For example, if the price makes a new high but the MFO does not, it suggests that the move lacks underlying strength, potentially signaling a reversal.
This concept is rooted in the idea that strong trends should be supported by increasing mass flow, and a failure to do so indicates a weakening trend.
Dynamic Risk Management:
The indicator adjusts the risk-to-reward (R:R) ratio of trades based on the strength of the trend (measured by energy). Stronger trends warrant a higher TP distance (better R:R), while weaker trends use a closer TP to increase the likelihood of hitting the target, reflecting a practical application of the strength analysis.
The overarching goal of the indicator is to provide traders with a physics-inspired framework to identify high-probability trading opportunities, filter out noise, and manage risk dynamically based on the market’s underlying strength.
ATR in Pips Every 14 CandlesATR in Pips Every 14 Candles is a custom TradingView indicator designed to provide periodic snapshots of market volatility in terms of pips. It calculates the Average True Range (ATR) using a user-configurable period and then converts the resulting value into pips based on a specified pip value. Every 14 candles, the indicator displays a label directly on the chart (positioned above the corresponding candle) that shows the current ATR range in pips. This setup is particularly useful for forex traders, as it translates volatility into familiar pip measurements and helps in setting stop-loss and take-profit levels based on the ATR range.
Daily ATR Based Grid with Multiple LevelsThis indicator plots a dynamic grid of horizontal levels on your chart based on the Average True Range (ATR) calculated on the daily timeframe. The grid levels are designed to help traders visualize key support and resistance areas that adjust dynamically based on market volatility. The number of grid levels, ATR length, and ATR multiplier can be customized to suit your trading strategy.
Key Features:
Daily ATR Calculation:
The ATR is calculated on the daily timeframe, ensuring the grid levels are based on daily volatility, regardless of the chart's current timeframe.
Customizable Grid Levels:
Users can specify the number of grid levels to plot above and below the current price.
The distance between grid levels is determined by the ATR multiplier, allowing for flexible adjustments.
Dynamic and Adaptive:
The grid levels adjust automatically as the price and daily ATR change, making it suitable for both trending and ranging markets.
Clean and Simple Visualization:
The grid levels are plotted as horizontal lines on the chart, extending infinitely in both directions for easy reference.
Input Parameters:
ATR Length:
The number of bars used to calculate the ATR. Default is 14.
ATR Multiplier:
The multiplier applied to the ATR to determine the distance between grid levels. Default is 1.5.
Number of Grid Levels:
The number of grid levels to plot above and below the current price. Default is 3.
Grid Color:
The color of the grid lines. Default is blue.
Grid Line Width:
The width of the grid lines. Default is 1.
How It Works:
The script fetches the daily ATR value using the request.security() function.
It calculates the distance between grid levels by multiplying the daily ATR by the ATR multiplier.
Using a loop, the script plots the specified number of grid levels above and below the current price.
The grid levels are updated dynamically as the price and ATR change.
Example Use Case:
If the daily ATR is 10, the ATR multiplier is 1.5, and the number of grids is 3, the script will plot the following levels:
Upper Levels: +15, +30, +45 points above the current price.
Lower Levels: -15, -30, -45 points below the current price.
These levels can act as potential support and resistance areas, helping traders identify entry, exit, and stop-loss points.
How to Use:
Add the indicator to your chart.
Customize the input parameters (ATR Length, ATR Multiplier, Number of Grid Levels, etc.) to suit your trading strategy.
Use the grid levels as reference points for:
Support and Resistance: Identify key levels where price might reverse or consolidate.
Take-Profit and Stop-Loss: Set targets based on the grid levels.
Trend Confirmation: Use the grid levels to confirm the strength of a trend.
Why Use This Indicator?:
Volatility-Based: The grid levels adjust based on market volatility, making them more relevant in different market conditions.
Customizable: Traders can adjust the number of levels and the distance between them to fit their trading style.
Timeframe-Independent: The ATR is calculated on the daily timeframe, making the indicator suitable for use on any chart timeframe.
ATR Percentagethis small indicator show on top right of the screen the % of the ATR instead of number so you can put it in %
Bollinger Bands Breakout Strategy// ©
// Bollinger Bands Breakout Strategy
// 🔹 Strategy Overview:
// This strategy trades **breakouts** using Bollinger Bands. It enters long when the price breaks above the upper band and enters short when the price breaks below the lower band. The strategy includes a take profit and stop loss mechanism to manage risk.
// 📌 When to BUY (Long Entry):
// ✅ Price **closes above the upper Bollinger Band** (breakout signal).
// ✅ Confirms a strong bullish trend, suitable for trending markets.
// 📌 When to SELL (Short Entry):
// ✅ Price **closes below the lower Bollinger Band** (breakdown signal).
// ✅ Indicates a strong bearish trend, ideal for volatile conditions.
// ⚠️ When to AVOID Trading:
// ❌ Sideways or ranging markets where price frequently **touches bands but reverses**.
// ❌ When volatility is **too low**, leading to false breakouts.
// ❌ During major news events that cause **sudden price spikes**.
// 🔧 Additional Considerations:
// - Best used in **trending markets** to avoid false breakouts.
// - Can be improved by adding **volume filters or trend confirmation (e.g., moving averages, RSI)**.
// - Adjust **Bollinger Bands settings (length & multiplier)** based on asset volatility.
// 🚀 Optimize this strategy by testing different timeframes & market conditions before live trading!
ETH/USDT EMA Crossover Strategy - OptimizedStrategy Name: EMA Crossover Strategy for ETH/USDT
Description:
This trading strategy is designed for the ETH/USDT pair and is based on exponential moving average (EMA) crossovers combined with momentum and volatility indicators. The strategy uses multiple filters to identify high-probability signals in both bullish and bearish trends, making it suitable for traders looking to trade in trending markets.
Strategy Components
EMAs (Exponential Moving Averages):
EMA 200: Used to identify the primary trend. If the price is above the EMA 200, it is considered a bullish trend; if below, a bearish trend.
EMA 50: Acts as an additional filter to confirm the trend.
EMA 20 and EMA 50 Short: These short-term EMAs generate entry signals through crossovers. A bullish crossover (EMA 20 crosses above EMA 50 Short) is a buy signal, while a bearish crossover (EMA 20 crosses below EMA 50 Short) is a sell signal.
RSI (Relative Strength Index):
The RSI is used to avoid overbought or oversold conditions. Long trades are only taken when the RSI is above 30, and short trades when the RSI is below 70.
ATR (Average True Range):
The ATR is used as a volatility filter. Trades are only taken when there is sufficient volatility, helping to avoid false signals in quiet markets.
Volume:
A volume filter is used to confirm sufficient market participation in the price movement. Trades are only taken when volume is above average.
Strategy Logic
Long Trades:
The price must be above the EMA 200 (bullish trend).
The EMA 20 must cross above the EMA 50 Short.
The RSI must be above 30.
The ATR must indicate sufficient volatility.
Volume must be above average.
Short Trades:
The price must be below the EMA 200 (bearish trend).
The EMA 20 must cross below the EMA 50 Short.
The RSI must be below 70.
The ATR must indicate sufficient volatility.
Volume must be above average.
How to Use the Strategy
Setup:
Add the script to your ETH/USDT chart on TradingView.
Adjust the parameters according to your preferences (e.g., EMA periods, RSI, ATR, etc.).
Signals:
Buy and sell signals will be displayed directly on the chart.
Long trades are indicated with an upward arrow, and short trades with a downward arrow.
Risk Management:
Use stop-loss and take-profit orders in all trades.
Consider a risk-reward ratio of at least 1:2.
Backtesting:
Test the strategy on historical data to evaluate its performance before using it live.
Advantages of the Strategy
Trend-focused: The strategy is designed to trade in trending markets, increasing the probability of success.
Multiple filters: The use of RSI, ATR, and volume reduces false signals.
Adaptability: It can be adjusted for different timeframes, although it is recommended to test it on 5-minute and 15-minute charts for ETH/USDT.
Warnings
Sideways markets: The strategy may generate false signals in markets without a clear trend. It is recommended to avoid trading in such conditions.
Optimization: Make sure to optimize the parameters according to the market and timeframe you are using.
Risk management: Never trade without stop-loss and take-profit orders.
Author
Jose J. Sanchez Cuevas
Version
v1.0
